{"id":"W2903826698","doi":"10.1109/naecon.2018.8556705","title":"A Low-Complexity Nonparametric STAP Detector","year":2018,"lang":"en","type":"article","venue":"","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Covariance matrix; Clutter; Algorithm; Space-time adaptive processing; Covariance; Computer science; Estimation of covariance matrices; Computational complexity theory; Weight; Radar; Detector; Nonparametric statistics; Adaptive filter; Mathematics; Mathematical optimization; Statistics; Radar engineering details; Radar imaging; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008265696,0.00008680221,0.0001079092,0.00008637243,0.00005999253,0.00005307487,0.00009314307,0.0000406962,0.0004419259],"category_scores_gemma":[0.00001609801,0.00007281185,0.00002874576,0.0003233374,0.00004025387,0.00009575764,0.00001126321,0.00006295513,0.000409224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003275804,"about_ca_system_score_gemma":0.00000867107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004120367,"about_ca_topic_score_gemma":0.00005058408,"domain_scores_codex":[0.9994615,0.00000713418,0.000135875,0.0001002406,0.0001098468,0.0001853703],"domain_scores_gemma":[0.99974,0.00001934372,0.00001174693,0.0001303005,0.00003680167,0.0000617962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008456032,0.0002446883,0.003905121,0.002824524,0.000327133,0.00007815208,0.003063364,0.004347889,0.3983212,0.008246562,0.07694709,0.5016097],"study_design_scores_gemma":[0.0008433097,0.0002511866,0.006476467,0.0001430908,0.00002017853,0.00003990676,0.00021641,0.5564849,0.4013065,0.002225114,0.03097496,0.001018044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7635165,0.0001820132,0.139078,0.00001075878,0.0004845501,0.00008781371,0.000002208678,0.0005719889,0.09606614],"genre_scores_gemma":[0.996106,0.000001402886,0.003210792,0.00003028158,0.0002785516,0.000002814545,4.276282e-7,0.00001851639,0.0003511847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.552137,"threshold_uncertainty_score":0.5259883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214496049770034,"score_gpt":0.2315328077986904,"score_spread":0.2093878473009901,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}